KEGG

KEGG: Kyoto Encyclopedia of Genes and Genomes. KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from genomic and molecular-level information. It is a computer representation of the biological system, consisting of molecular building blocks of genes and proteins (genomic information) and chemical substances (chemical information) that are integrated with the knowledge on molecular wiring diagrams of interaction, reaction and relation networks (systems information). It also contains disease and drug information (health information) as perturbations to the biological system.


References in zbMATH (referenced in 181 articles )

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  1. Jordi Martorell-Marugán, Víctor González-Rumayor, Pedro Carmona-Sáez: mCSEA: detecting subtle differentially methylated regions (2019) not zbMATH
  2. Kralj, Jan; Robnik-Sikonja, Marko; Lavrac, Nada: NetSDM: semantic data mining with network analysis (2019)
  3. Kuan-Hao Chao, Yi-Wen Hsiao, Yi-Fang Lee, Chien-Yueh Lee, Liang-Chuan Lai, Mong-Hsun Tsai, Tzu-Pin Lu, Eric Y. Chuang: RNASeqR: an R package for automated two-group RNA-Seq analysis workflow (2019) arXiv
  4. Rayhan, Farshid; Ahmed, Sajid; Md Farid, Dewan; Dehzangi, Abdollah; Shatabda, Swakkhar: CFSBoost: cumulative feature subspace boosting for drug-target interaction prediction (2019)
  5. Sanguinetti, Guido (ed.); Huynh-Thu, Vân Anh (ed.): Gene regulatory networks. Methods and protocols (2019)
  6. Esteves, Gustavo H.; Reis, Luiz F. L.: A statistical method for measuring activation of gene regulatory networks (2018)
  7. Franks, Alexander M.; Markowetz, Florian; Airoldi, Edoardo M.: Refining cellular pathway models using an ensemble of heterogeneous data sources (2018)
  8. Latif, Majid jun.; May, Elebeoba E.: A multiscale agent-based model for the investigation of E. coli K12 metabolic response during biofilm formation (2018)
  9. Lee, JungJun; Kim, SungHwan; Jhong, Jae-Hwan; Koo, Ja-Yong: Variable selection and joint estimation of mean and covariance models with an application to eQTL data (2018)
  10. Li, Quefeng; Cheng, Guang; Fan, Jianqing; Wang, Yuyan: Embracing the blessing of dimensionality in factor models (2018)
  11. Luo, Xiangyu; Wei, Yingying: Nonparametric Bayesian learning of heterogeneous dynamic transcription factor networks (2018)
  12. Sun, Jiehuan; Herazo-Maya, Jose D.; Huang, Xiu; Kaminski, Naftali; Zhao, Hongyu: Distance-correlation based gene set analysis in longitudinal studies (2018)
  13. von Stechow, Louise (ed.); Delgado, Alberto Santos (ed.): Computational cell biology. Methods and protocols (2018)
  14. Yang, Eunho; Lozano, Aurélie C.; Aravkin, Aleksandr: A general family of trimmed estimators for robust high-dimensional data analysis (2018)
  15. Yuan, Fei; Lu, Lin; Zhang, YuHang; Wang, ShaoPeng; Cai, Yu-Dong: Data mining of the cancer-related lncRNAs GO terms and KEGG pathways by using mRMR method (2018)
  16. Zhao, Xian; Chen, Lei; Lu, Jing: A similarity-based method for prediction of drug side effects with heterogeneous information (2018)
  17. Álvarez-Miranda, Eduardo; Farhan, Hesso; Luipersbeck, Martin; Sinnl, Markus: A bi-objective network design approach for discovering functional modules linking Golgi apparatus fragmentation and neuronal death (2017)
  18. Angelopoulos, Nicos; Cussens, James: Distributional logic programming for Bayesian knowledge representation (2017)
  19. Barish, Robert D.; Suyama, Akira: Counting substrate cycles in topologically restricted metabolic networks (2017)
  20. Friedrichs, Stefanie; Manitz, Juliane; Burger, Patricia; Amos, Christopher I.; Risch, Angela; Chang-Claude, Jenny; Wichmann, Heinz-Erich; Kneib, Thomas; Bickeböller, Heike; Hofner, Benjamin: Pathway-based kernel boosting for the analysis of genome-wide association studies (2017)

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